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| 3D Model Retrieval with Multi-Granular Semantics Based on Gaussian Process Classifier |
| GAO Bo-Yong1,2, ZHANG San-Yuan1, PAN Xiang3 |
1. Department of Computer Science and Engineering, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027
2.Department of Computer Science and Technology, College of Information Engineering, China Jiliang University, Hangzhou 310018
3. Department of Computer Science and Technology, Institute of Computer Science, Zhejiang University of Technology, Hangzhou 310014 |
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Abstract In order to solve the inconsistency between users’ intentions in semantic 3D model retrieval system, a retrieval framework with multi-granular semantics is established, in which learning model can adapt to different user search intentions. Firstly, model classification is divided into different levels and the multi-granularity structure of semantic concept is formed. Then, a hybrid shape feature based on views is used to describe the shape characteristics of 3D model. And the Gaussian process classifier is used to associate low-level features with query concepts on a different level of semantic concept. Compared with existing research, the retrieval framework with multi-granular semantics allows the users to set their retrieval intentions according to selecting the granular level of semantics, and the results meet the user semantics as much as possible. The experimental results of retrieval performance evaluation using the benchmark show that the retrieval performance using proposed method is significantly higher than content-based retrieval and confident with human concept.
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Received: 09 September 2010
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2011, Vol. 24 
